Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.
Estimated Time Needed: 30 min
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import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.
tesla = yf.Ticker('TSLA')
Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.
tesla_data = tesla.history(period="max")
Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.
tesla_data.reset_index(inplace=True)
tesla_data.head(5)
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0 | 0.0 |
| 1 | 2010-06-30 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0 | 0.0 |
| 2 | 2010-07-01 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0 | 0.0 |
| 3 | 2010-07-02 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0 | 0.0 |
| 4 | 2010-07-06 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0 | 0.0 |
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.
url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue"
html_data = requests.get(url).text
Parse the html data using beautiful_soup.
soup = BeautifulSoup(html_data, "html5lib")
print(soup.prettify())
Using BeautifulSoup or the read_html function extract the table with Tesla Quarterly Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
tesla_revenue = pd.DataFrame(columns = ["Date","Revenue"])
for table in soup.find_all('table'):
if table.find('th').getText().startswith("Tesla Quarterly Revenue"):
for row in table.find("tbody").find_all("tr"):
col = row.find_all("td")
if len(col) != 2: continue
Date = col[0].text
Revenue = col[1].text.replace("$","").replace(",","")
tesla_revenue = tesla_revenue.append({"Date":Date, "Revenue":Revenue}, ignore_index=True)
Execute the following line to remove the comma and dollar sign from the Revenue column.
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")
/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. """Entry point for launching an IPython kernel.
Execute the following lines to remove an null or empty strings in the Revenue column.
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.
tesla_revenue.tail(5)
| Date | Revenue | |
|---|---|---|
| 50 | 2010-09-30 | 31 |
| 51 | 2010-06-30 | 28 |
| 52 | 2010-03-31 | 21 |
| 54 | 2009-09-30 | 46 |
| 55 | 2009-06-30 | 27 |
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.
gme = yf.Ticker('GME')
Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.
gme_data = gme.history(period = "max")
Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.
gme_data.reset_index(inplace=True)
gme_data.head(5)
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 | 1.620128 | 1.693350 | 1.603296 | 1.691666 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 | 1.712707 | 1.716073 | 1.670626 | 1.683250 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 | 1.683251 | 1.687459 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 | 1.615920 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data.
url = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue"
html_data = requests.get(url).text
Parse the html data using beautiful_soup.
soup = BeautifulSoup(html_data, "html5lib")
print(soup.prettify())
<!DOCTYPE html>
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<link href="https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue" rel="canonical"/>
<title>
GameStop Revenue 2010-2023 | GME | MacroTrends
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<meta content="GameStop annual/quarterly revenue history and growth rate from 2010 to 2023. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.
<ul style='margin-top:10px;'>
<li>GameStop revenue for the quarter ending January 31, 2023 was <strong>$2.226B</strong>, a <strong>1.22% decline</strong> year-over-year.</li>
<li>GameStop revenue for the twelve months ending January 31, 2023 was <strong>$5.927B</strong>, a <strong>1.39% decline</strong> year-over-year.</li>
<li>GameStop annual revenue for 2023 was <strong>$5.927B</strong>, a <strong>1.39% decline</strong> from 2022.</li>
<li>GameStop annual revenue for 2022 was <strong>$6.011B</strong>, a <strong>18.09% increase</strong> from 2021.</li>
<li>GameStop annual revenue for 2021 was <strong>$5.09B</strong>, a <strong>21.28% decline</strong> from 2020.</li>
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<meta content="GameStop Revenue 2010-2023 | GME" name="twitter:title"/>
<meta content="GameStop annual/quarterly revenue history and growth rate from 2010 to 2023. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.
<ul style='margin-top:10px;'>
<li>GameStop revenue for the quarter ending January 31, 2023 was <strong>$2.226B</strong>, a <strong>1.22% decline</strong> year-over-year.</li>
<li>GameStop revenue for the twelve months ending January 31, 2023 was <strong>$5.927B</strong>, a <strong>1.39% decline</strong> year-over-year.</li>
<li>GameStop annual revenue for 2023 was <strong>$5.927B</strong>, a <strong>1.39% decline</strong> from 2022.</li>
<li>GameStop annual revenue for 2022 was <strong>$6.011B</strong>, a <strong>18.09% increase</strong> from 2021.</li>
<li>GameStop annual revenue for 2021 was <strong>$5.09B</strong>, a <strong>21.28% decline</strong> from 2020.</li>
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<meta content="GameStop annual/quarterly revenue history and growth rate from 2010 to 2023. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.
<ul style='margin-top:10px;'>
<li>GameStop revenue for the quarter ending January 31, 2023 was <strong>$2.226B</strong>, a <strong>1.22% decline</strong> year-over-year.</li>
<li>GameStop revenue for the twelve months ending January 31, 2023 was <strong>$5.927B</strong>, a <strong>1.39% decline</strong> year-over-year.</li>
<li>GameStop annual revenue for 2023 was <strong>$5.927B</strong>, a <strong>1.39% decline</strong> from 2022.</li>
<li>GameStop annual revenue for 2022 was <strong>$6.011B</strong>, a <strong>18.09% increase</strong> from 2021.</li>
<li>GameStop annual revenue for 2021 was <strong>$5.09B</strong>, a <strong>21.28% decline</strong> from 2020.</li>
</ul>" property="og:description"/>
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GameStop Revenue 2010-2023 | GME
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GameStop annual/quarterly revenue history and growth rate from 2010 to 2023. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.
<ul style="margin-top:10px;">
<li>
GameStop revenue for the quarter ending January 31, 2023 was
<strong>
$2.226B
</strong>
, a
<strong>
1.22% decline
</strong>
year-over-year.
</li>
<li>
GameStop revenue for the twelve months ending January 31, 2023 was
<strong>
$5.927B
</strong>
, a
<strong>
1.39% decline
</strong>
year-over-year.
</li>
<li>
GameStop annual revenue for 2023 was
<strong>
$5.927B
</strong>
, a
<strong>
1.39% decline
</strong>
from 2022.
</li>
<li>
GameStop annual revenue for 2022 was
<strong>
$6.011B
</strong>
, a
<strong>
18.09% increase
</strong>
from 2021.
</li>
<li>
GameStop annual revenue for 2021 was
<strong>
$5.09B
</strong>
, a
<strong>
21.28% decline
</strong>
from 2020.
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GameStop Annual Revenue
<br/>
<span style="font-size:14px;">
(Millions of US $)
</span>
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2023
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$5,927
</td>
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2022
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$6,011
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2021
</td>
<td style="text-align:center">
$5,090
</td>
</tr>
<tr>
<td style="text-align:center">
2020
</td>
<td style="text-align:center">
$6,466
</td>
</tr>
<tr>
<td style="text-align:center">
2019
</td>
<td style="text-align:center">
$8,285
</td>
</tr>
<tr>
<td style="text-align:center">
2018
</td>
<td style="text-align:center">
$8,547
</td>
</tr>
<tr>
<td style="text-align:center">
2017
</td>
<td style="text-align:center">
$7,965
</td>
</tr>
<tr>
<td style="text-align:center">
2016
</td>
<td style="text-align:center">
$9,364
</td>
</tr>
<tr>
<td style="text-align:center">
2015
</td>
<td style="text-align:center">
$9,296
</td>
</tr>
<tr>
<td style="text-align:center">
2014
</td>
<td style="text-align:center">
$9,040
</td>
</tr>
<tr>
<td style="text-align:center">
2013
</td>
<td style="text-align:center">
$8,887
</td>
</tr>
<tr>
<td style="text-align:center">
2012
</td>
<td style="text-align:center">
$9,551
</td>
</tr>
<tr>
<td style="text-align:center">
2011
</td>
<td style="text-align:center">
$9,474
</td>
</tr>
<tr>
<td style="text-align:center">
2010
</td>
<td style="text-align:center">
$9,078
</td>
</tr>
<tr>
<td style="text-align:center">
2009
</td>
<td style="text-align:center">
$8,806
</td>
</tr>
</tbody>
</table>
</div>
<div class="col-xs-6">
<table class="historical_data_table table">
<thead>
<tr>
<th colspan="2" style="text-align:center">
GameStop Quarterly Revenue
<br/>
<span style="font-size:14px;">
(Millions of US $)
</span>
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">
2023-01-31
</td>
<td style="text-align:center">
$2,226
</td>
</tr>
<tr>
<td style="text-align:center">
2022-10-31
</td>
<td style="text-align:center">
$1,186
</td>
</tr>
<tr>
<td style="text-align:center">
2022-07-31
</td>
<td style="text-align:center">
$1,136
</td>
</tr>
<tr>
<td style="text-align:center">
2022-04-30
</td>
<td style="text-align:center">
$1,378
</td>
</tr>
<tr>
<td style="text-align:center">
2022-01-31
</td>
<td style="text-align:center">
$2,254
</td>
</tr>
<tr>
<td style="text-align:center">
2021-10-31
</td>
<td style="text-align:center">
$1,297
</td>
</tr>
<tr>
<td style="text-align:center">
2021-07-31
</td>
<td style="text-align:center">
$1,183
</td>
</tr>
<tr>
<td style="text-align:center">
2021-04-30
</td>
<td style="text-align:center">
$1,277
</td>
</tr>
<tr>
<td style="text-align:center">
2021-01-31
</td>
<td style="text-align:center">
$2,122
</td>
</tr>
<tr>
<td style="text-align:center">
2020-10-31
</td>
<td style="text-align:center">
$1,005
</td>
</tr>
<tr>
<td style="text-align:center">
2020-07-31
</td>
<td style="text-align:center">
$942
</td>
</tr>
<tr>
<td style="text-align:center">
2020-04-30
</td>
<td style="text-align:center">
$1,021
</td>
</tr>
<tr>
<td style="text-align:center">
2020-01-31
</td>
<td style="text-align:center">
$2,194
</td>
</tr>
<tr>
<td style="text-align:center">
2019-10-31
</td>
<td style="text-align:center">
$1,439
</td>
</tr>
<tr>
<td style="text-align:center">
2019-07-31
</td>
<td style="text-align:center">
$1,286
</td>
</tr>
<tr>
<td style="text-align:center">
2019-04-30
</td>
<td style="text-align:center">
$1,548
</td>
</tr>
<tr>
<td style="text-align:center">
2019-01-31
</td>
<td style="text-align:center">
$3,063
</td>
</tr>
<tr>
<td style="text-align:center">
2018-10-31
</td>
<td style="text-align:center">
$1,935
</td>
</tr>
<tr>
<td style="text-align:center">
2018-07-31
</td>
<td style="text-align:center">
$1,501
</td>
</tr>
<tr>
<td style="text-align:center">
2018-04-30
</td>
<td style="text-align:center">
$1,786
</td>
</tr>
<tr>
<td style="text-align:center">
2018-01-31
</td>
<td style="text-align:center">
$2,825
</td>
</tr>
<tr>
<td style="text-align:center">
2017-10-31
</td>
<td style="text-align:center">
$1,989
</td>
</tr>
<tr>
<td style="text-align:center">
2017-07-31
</td>
<td style="text-align:center">
$1,688
</td>
</tr>
<tr>
<td style="text-align:center">
2017-04-30
</td>
<td style="text-align:center">
$2,046
</td>
</tr>
<tr>
<td style="text-align:center">
2017-01-31
</td>
<td style="text-align:center">
$2,403
</td>
</tr>
<tr>
<td style="text-align:center">
2016-10-31
</td>
<td style="text-align:center">
$1,959
</td>
</tr>
<tr>
<td style="text-align:center">
2016-07-31
</td>
<td style="text-align:center">
$1,632
</td>
</tr>
<tr>
<td style="text-align:center">
2016-04-30
</td>
<td style="text-align:center">
$1,972
</td>
</tr>
<tr>
<td style="text-align:center">
2016-01-31
</td>
<td style="text-align:center">
$3,525
</td>
</tr>
<tr>
<td style="text-align:center">
2015-10-31
</td>
<td style="text-align:center">
$2,016
</td>
</tr>
<tr>
<td style="text-align:center">
2015-07-31
</td>
<td style="text-align:center">
$1,762
</td>
</tr>
<tr>
<td style="text-align:center">
2015-04-30
</td>
<td style="text-align:center">
$2,061
</td>
</tr>
<tr>
<td style="text-align:center">
2015-01-31
</td>
<td style="text-align:center">
$3,476
</td>
</tr>
<tr>
<td style="text-align:center">
2014-10-31
</td>
<td style="text-align:center">
$2,092
</td>
</tr>
<tr>
<td style="text-align:center">
2014-07-31
</td>
<td style="text-align:center">
$1,731
</td>
</tr>
<tr>
<td style="text-align:center">
2014-04-30
</td>
<td style="text-align:center">
$1,996
</td>
</tr>
<tr>
<td style="text-align:center">
2014-01-31
</td>
<td style="text-align:center">
$3,684
</td>
</tr>
<tr>
<td style="text-align:center">
2013-10-31
</td>
<td style="text-align:center">
$2,107
</td>
</tr>
<tr>
<td style="text-align:center">
2013-07-31
</td>
<td style="text-align:center">
$1,384
</td>
</tr>
<tr>
<td style="text-align:center">
2013-04-30
</td>
<td style="text-align:center">
$1,865
</td>
</tr>
<tr>
<td style="text-align:center">
2013-01-31
</td>
<td style="text-align:center">
$3,562
</td>
</tr>
<tr>
<td style="text-align:center">
2012-10-31
</td>
<td style="text-align:center">
$1,773
</td>
</tr>
<tr>
<td style="text-align:center">
2012-07-31
</td>
<td style="text-align:center">
$1,550
</td>
</tr>
<tr>
<td style="text-align:center">
2012-04-30
</td>
<td style="text-align:center">
$2,002
</td>
</tr>
<tr>
<td style="text-align:center">
2012-01-31
</td>
<td style="text-align:center">
$3,579
</td>
</tr>
<tr>
<td style="text-align:center">
2011-10-31
</td>
<td style="text-align:center">
$1,947
</td>
</tr>
<tr>
<td style="text-align:center">
2011-07-31
</td>
<td style="text-align:center">
$1,744
</td>
</tr>
<tr>
<td style="text-align:center">
2011-04-30
</td>
<td style="text-align:center">
$2,281
</td>
</tr>
<tr>
<td style="text-align:center">
2011-01-31
</td>
<td style="text-align:center">
$3,693
</td>
</tr>
<tr>
<td style="text-align:center">
2010-10-31
</td>
<td style="text-align:center">
$1,899
</td>
</tr>
<tr>
<td style="text-align:center">
2010-07-31
</td>
<td style="text-align:center">
$1,799
</td>
</tr>
<tr>
<td style="text-align:center">
2010-04-30
</td>
<td style="text-align:center">
$2,083
</td>
</tr>
<tr>
<td style="text-align:center">
2010-01-31
</td>
<td style="text-align:center">
$3,524
</td>
</tr>
<tr>
<td style="text-align:center">
2009-10-31
</td>
<td style="text-align:center">
$1,835
</td>
</tr>
<tr>
<td style="text-align:center">
2009-07-31
</td>
<td style="text-align:center">
$1,739
</td>
</tr>
<tr>
<td style="text-align:center">
2009-04-30
</td>
<td style="text-align:center">
$1,981
</td>
</tr>
<tr>
<td style="text-align:center">
2009-01-31
</td>
<td style="text-align:center">
$3,492
</td>
</tr>
</tbody>
</table>
</div>
</div>
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<th style="text-align:center">
Sector
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<th style="text-align:center">
Industry
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<th style="text-align:center">
Market Cap
</th>
<th style="text-align:center">
Revenue
</th>
</tr>
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<tr>
<td style="text-align:center">
<a href="https://www.macrotrends.net/stocks/sector/3/retail-wholesale">
Retail/Wholesale
</a>
</td>
<td style="text-align:center">
<a href="https://www.macrotrends.net/stocks/industry/156/">
Retail - Consumer Electronics
</a>
</td>
<td style="text-align:center">
$6.222B
</td>
<td style="text-align:center">
$5.927B
</td>
</tr>
<tr>
<td colspan="4" style="padding:15px;">
<span>
GameStop Corp. offers the best selection of new and pre-owned video gaming consoles, accessories and video game titles, in both physical and digital formats. The company sells various types of digital products, including downloadable content, network points cards, prepaid digital, prepaid subscription cards and digitally downloadable software as well as collectible products. The company also publishes Game Informer, the print and digital video game publication. The company's products are categorized as follows: Hardware and Accessories - GameStop offers new and pre-owned video game platforms from the major console and PC manufacturers. Accessories consist primarily of controllers, gaming headsets, virtual reality products and memory cards. Software: The company provides new and pre-owned video game software for current and certain prior generation consoles. Collectibles: The category consists of licensed merchandise, primarily related to the video game, television and movie industries and pop-culture themes.
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<th style="text-align:center; width:20%;">
Market Cap
</th>
<th style="text-align:center; width:20%;">
PE Ratio
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">
<a href="/stocks/charts/BBY/best-buy/revenue">
Best Buy (BBY)
</a>
</td>
<td style="text-align:center">
United States
</td>
<td style="text-align:center">
$16.087B
</td>
<td style="text-align:center">
10.36
</td>
</tr>
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Using BeautifulSoup or the read_html function extract the table with GameStop Quarterly Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
gme_revenue = pd.DataFrame(columns = ["Date","Revenue"])
for table in soup.find_all('table'):
if table.find('th').getText().startswith("GameStop Quarterly Revenue"):
for row in table.find("tbody").find_all("tr"):
col = row.find_all("td")
if len(col) != 2: continue
Date = col[0].text
Revenue = col[1].text.replace("$","").replace(",","")
gme_revenue = gme_revenue.append({"Date":Date, "Revenue":Revenue}, ignore_index=True)
Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.
gme_revenue.tail(5)
| Date | Revenue | |
|---|---|---|
| 52 | 2010-01-31 | 3524 |
| 53 | 2009-10-31 | 1835 |
| 54 | 2009-07-31 | 1739 |
| 55 | 2009-04-30 | 1981 |
| 56 | 2009-01-31 | 3492 |
Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla'). Note the graph will only show data upto June 2021.
make_graph(tesla_data, tesla_revenue, 'Tesla')
Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.
make_graph(gme_data, gme_revenue, 'GameStop')
Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Azim Hirjani
| Date (YYYY-MM-DD) | Version | Changed By | Change Description |
|---|---|---|---|
| 2022-02-28 | 1.2 | Lakshmi Holla | Changed the URL of GameStop |
| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |
| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |